JOURNAL ARTICLE

The Influence of Karma Beliefs in Quality of Commitment, Forgiveness and Altruism in Romantic Relationships.

  • Published In: Indian Journal of Health & Wellbeing, 2025, v. 16, n. 4-II. P. 878 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Khandale, Natalie Sharma; Yadav, Kaustubh V. 3 of 3

Abstract

This study investigates the correlation between Karma beliefs, Quality of Commitment, Forgiveness and Altruism in Romantic relationships among young adults, from the ages 18-30. It aims to study how belief in Karma can influence the variables of Quality of Commitment, Forgiveness and Altruism in romantic relationships, given the marked lack of research exploring these variables. 178 participants who fit the criteria mentioned were selected through surveys for statistical analysis. The tools used were- Belief in Karma Questionnaire (White, Norenzayan, & Schaller, 2019), Revised Commitment Inventory (Owens, Rhodes, Stanely, & Markman, 2011), Bolton Forgiveness Scale (Amanze & Carson, 2019), and Self-Report Altruism Scale (Manzur & Olvaerrieta, 1998). The results showed a significant negative correlation between Karma beliefs and Quality of Commitment, Karma beliefs and Forgiveness and Karma beliefs and Altruism. There has been a notable gap in the study of Karma in the context of modern world concepts and ideologies, this research could serve as an impetus for contributing research in this domain. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Indian Journal of Health & Wellbeing. 2025/12, Vol. 16, Issue 4-II, p878
  • Document Type:Article
  • Subject Area:Arts and Entertainment
  • Publication Date:2025
  • ISSN:2229-5356
  • Accession Number:190945690
  • Copyright Statement:Copyright of Indian Journal of Health & Wellbeing is the property of Indian Association of Health, Research & Welfare and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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